Diversity, equity and inclusive lessons from a workplace in the Canadian Arctic
Bibliographic record
Abstract
This chapter is about leadership that supports diversity, equity and inclusion (DEI), and its challenges in the context of Inuit and multicultural workplaces in the newest and largest territory in Canada, Nunavut, more specifically in the field of social services. The workforce is diverse, consisting of Inuit employees (30-60%) and others (40-70%) who are of varied ethno-cultural backgrounds, gender, abilities, immigration status and sexual orientation. The research method utilized is reflective practice that is aligned with, both, Inuit Societal Values and social work practice in cross cultural settings thus complementing and supporting the natural, non-intrusive and non-interference manner of which the research is conducted. The lessons learned are: the challenges of DEI workplaces are varied and complex, however, embedding cultural values and leading in a culturally congruent and safe manner creates the environment that supports employees of diverse backgrounds. Further, shared leadership, that is based on mutual respect and appreciation of diverse skills and knowledge appears to be the most common and successful approach while firmly rooted in the Inuit culture. Recommendations for future research is expending and complementing lessons learned by conducting participative research through action learning, action research, in order to bring Indigenous and multicultural voices forward, further informing specific strategies aligned and supportive of the principles of diversity, equity and inclusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.027 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.028 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".